An LSTM-autoencoder based online side channel monitoring approach for cyber-physical attack detection in additive manufacturing

Additive manufacturing (AM) has gained increasing popularity in a large variety of mission-critical fields, such as aerospace, medical, and transportation. The layer-by-layer fabrication scheme of the AM significantly enhances fabrication flexibility, resulting in the expanded vulnerability space of...

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Veröffentlicht in:Journal of intelligent manufacturing Jg. 34; H. 4; S. 1815 - 1831
Hauptverfasser: Shi, Zhangyue, Mamun, Abdullah Al, Kan, Chen, Tian, Wenmeng, Liu, Chenang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.04.2023
Springer Nature B.V
Schlagworte:
Accelerometers
Additive manufacturing
Advanced manufacturing technologies
Aerospace engineering
Business and Management
Case studies
Control
Data analysis
Fabrication
Feature extraction
Fused deposition modeling
Information sharing
Machine learning
Machines
Manufacturing
Mechanical properties
Mechatronics
Methods
Monitoring
Monitoring methods
Processes
Product design
Production
Quality control
Robotics
Sensors
Systems engineering
Thickness
Online attack detection
LSTM-autoencoder
Process authentication
Additive manufacturing
Side channel
Cyber-physical security
ISSN:0956-5515, 1572-8145
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Zusammenfassung:Additive manufacturing (AM) has gained increasing popularity in a large variety of mission-critical fields, such as aerospace, medical, and transportation. The layer-by-layer fabrication scheme of the AM significantly enhances fabrication flexibility, resulting in the expanded vulnerability space of cyber-physical AM systems. This potentially leads to altered AM parts with compromised mechanical properties and functionalities. Furthermore, those internal alterations in the AM builds are very challenging to detect using the traditional geometric dimensioning and tolerancing (GD&T) features. Therefore, how to effectively monitor and accurately detect cyber-physical attacks becomes a critical barrier for the broader adoption of AM technology. To address this issue, this paper proposes a machine learning-driven online side channel monitoring approach for AM process authentication. A data-driven feature extraction approach based on the LSTM-autoencoder is developed to detect the unintended process/product alterations caused by cyber-physical attacks. Both supervised and unsupervised monitoring schemes are implemented based on the extracted features. To validate the effectiveness of the proposed method, real-world case studies were conducted using a fused filament fabrication (FFF) platform equipped with two accelerometers. In the case study, two different types of cyber-physical attacks are implemented to mimic the potential real-world process alterations. Experimental results demonstrate that the proposed method outperforms conventional process monitoring methods, and it can effectively detect part geometry and layer thickness alterations in a real-time manner.
Bibliographie:ObjectType-Article-1
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ISSN:0956-5515
1572-8145
DOI:10.1007/s10845-021-01879-9